{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:P6NI6L5KTOMAFCF3SHOCBBNZ6N","short_pith_number":"pith:P6NI6L5K","schema_version":"1.0","canonical_sha256":"7f9a8f2faa9b980288bb91dc2085b9f3503f67d3d695c06b1c308d6f3b9aa355","source":{"kind":"arxiv","id":"2605.29976","version":1},"attestation_state":"computed","paper":{"title":"Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.ao-ph","authors_text":"Antonia Jost, Christian Lessig, Claire Monteleoni, Guillaume Couairon, Renu Singh, Robert Brunstein, Thomas Rackow, Yana Hasson","submitted_at":"2026-05-28T14:15:25Z","abstract_excerpt":"We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather forecasting and evaluated up to a 10-day lead time. ArchesWeather is a deterministic model, while ArchesWeatherGen is a probabilistic flow-matching model leveraging ArchesWeather's forecasts, enabling ensemble-based uncertainty quantification. In this work, we adapt these models to act as forced atmospheric models by using additional conditioning on the monthly mean sea surface temperature (SST) and sea ice cover (SIC) as boundary conditions. In part"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.29976","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-28T14:15:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ca107a1e1946c111b14ebf286a225d36e82a3d5aa04b6fc0f02d33fc1c164a7a","abstract_canon_sha256":"9a723cb379d90348245391d25fa8b8415c989d8d5880b14f0fa1e2b6023d134d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:06:04.809421Z","signature_b64":"xrcGCBL22IMvEiexf1M2yFij90Zqd0NNFwOYgNJgW1jexIIhjP8YdmhlVifKGMHuYejqe3QYt9lISLWK2QY/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f9a8f2faa9b980288bb91dc2085b9f3503f67d3d695c06b1c308d6f3b9aa355","last_reissued_at":"2026-05-29T02:06:04.808583Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:06:04.808583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.ao-ph","authors_text":"Antonia Jost, Christian Lessig, Claire Monteleoni, Guillaume Couairon, Renu Singh, Robert Brunstein, Thomas Rackow, Yana Hasson","submitted_at":"2026-05-28T14:15:25Z","abstract_excerpt":"We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather forecasting and evaluated up to a 10-day lead time. ArchesWeather is a deterministic model, while ArchesWeatherGen is a probabilistic flow-matching model leveraging ArchesWeather's forecasts, enabling ensemble-based uncertainty quantification. In this work, we adapt these models to act as forced atmospheric models by using additional conditioning on the monthly mean sea surface temperature (SST) and sea ice cover (SIC) as boundary conditions. In part"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29976","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.29976/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.29976","created_at":"2026-05-29T02:06:04.808724+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.29976v1","created_at":"2026-05-29T02:06:04.808724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29976","created_at":"2026-05-29T02:06:04.808724+00:00"},{"alias_kind":"pith_short_12","alias_value":"P6NI6L5KTOMA","created_at":"2026-05-29T02:06:04.808724+00:00"},{"alias_kind":"pith_short_16","alias_value":"P6NI6L5KTOMAFCF3","created_at":"2026-05-29T02:06:04.808724+00:00"},{"alias_kind":"pith_short_8","alias_value":"P6NI6L5K","created_at":"2026-05-29T02:06:04.808724+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P6NI6L5KTOMAFCF3SHOCBBNZ6N","json":"https://pith.science/pith/P6NI6L5KTOMAFCF3SHOCBBNZ6N.json","graph_json":"https://pith.science/api/pith-number/P6NI6L5KTOMAFCF3SHOCBBNZ6N/graph.json","events_json":"https://pith.science/api/pith-number/P6NI6L5KTOMAFCF3SHOCBBNZ6N/events.json","paper":"https://pith.science/paper/P6NI6L5K"},"agent_actions":{"view_html":"https://pith.science/pith/P6NI6L5KTOMAFCF3SHOCBBNZ6N","download_json":"https://pith.science/pith/P6NI6L5KTOMAFCF3SHOCBBNZ6N.json","view_paper":"https://pith.science/paper/P6NI6L5K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.29976&json=true","fetch_graph":"https://pith.science/api/pith-number/P6NI6L5KTOMAFCF3SHOCBBNZ6N/graph.json","fetch_events":"https://pith.science/api/pith-number/P6NI6L5KTOMAFCF3SHOCBBNZ6N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P6NI6L5KTOMAFCF3SHOCBBNZ6N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P6NI6L5KTOMAFCF3SHOCBBNZ6N/action/storage_attestation","attest_author":"https://pith.science/pith/P6NI6L5KTOMAFCF3SHOCBBNZ6N/action/author_attestation","sign_citation":"https://pith.science/pith/P6NI6L5KTOMAFCF3SHOCBBNZ6N/action/citation_signature","submit_replication":"https://pith.science/pith/P6NI6L5KTOMAFCF3SHOCBBNZ6N/action/replication_record"}},"created_at":"2026-05-29T02:06:04.808724+00:00","updated_at":"2026-05-29T02:06:04.808724+00:00"}